Artificial intelligence and machine learning-driven design of self-healing biomedical composites

The integration of Artificial Intelligence (AI) and Machine Learning (ML) has significantly enhanced the development of self-healing composites, especially in biomedical fields including tissue engineering, medication delivery, and implantable devices. These materials are designed to self-repair damage, enhancing durability, patient safety, and operational reliability. Considering that traditional materials may deteriorate under physiological conditions, intelligent self-healing composites augmented by AI/ML offer a revolutionary alternative.

This work examines current progress in AI- and ML-facilitated design, selection, and optimization of self-healing composites for biomedical applications. Attention is directed toward the application of supervised and unsupervised learning methodologies - such as Bayesian optimization, neural networks, and support vector machines to improve healing efficiency by 30-50%, decrease formulation time by approximately 40%, and achieve predictive accuracies of over 90% regarding failure or healing behavior in specific studies.

The research examines ethical aspects, encompassing data protection, algorithmic transparency, and adherence to regulatory standards such as FDA and ISO 10,993. The paper emphasizes the transformational potential of AI/ML in facilitating intelligent, responsive, and patient-specific composite designs, while also addressing possible issues such as dataset bias and algorithmic opacity. The results indicate that AI-enhanced self-healing systems will be pivotal in the future of customized medicine.